Characterization of Pulmonary Nodules in Computed Tomography Images Based on Pseudo-Labeling Using Radiology Reports

A computer-aided diagnosis (CAD) system that characterizes nodules in medical images can help radiologists determine its malignancy. Preparing large volumes of labeled data for CAD systems, however, requires advanced medical knowledge. This makes it extremely difficult to develop such systems, despite their growing demand. In this paper, we propose a new training method to build an image classifier for characterization of nodules utilizing pseudo-labels, i.e., image labels automatically retrieved from radiology reports. A radiology report is a type of record in which radiologists present a summary of lesion characteristics and diagnosis. Labeling radiology reports is much easier than labeling radiology images, and can be done without high expertise. Using several thousand labeled reports, we constructed a hierarchical attention network-based text classifier to assign pseudo-labels of the characteristics of pulmonary nodules with high accuracy (macro F1-score of 0.941). Experimental results show that the image classifier trained with the pseudo-labels can achieve almost the same performance as the one trained with the labels annotated by radiologists: AUC 0.848 for the model trained with the pseudo-labels on 3,000 computed tomography (CT) images and 0.847 for the model trained with the manual labels on 800 CT images.

Automated summary

This study proposes a CAD system method that utilizes pseudo-labels for training, obtained by automatically extracting image labels from radiology reports. The experimental results show that the image classifier trained with pseudo-labels achieves similar performance as the one trained with manually annotated labels.